Intelligence in the Digital Economy. IGP

Mahesh Raisinghani

Opportunities, Limitations and Risk

IDEA GROUP PUBLISHINGHershey • London • Melbourne • Singapore



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Intelligence in the Digital Economy:
Opportunities, Limitations and Risk


FOREWORD
In the past years, research in the field of responsive business environments
has had many successes. The most significant of these has been the
development of powerful new tools and methodologies that advance the subject
of Business Intelligence (BI). This book provides the BI practitioner and
researcher with a comprehensive view of the current art and the possibilities of the subject.

Dr. Raisinghani and his colleagues delight us with a breadth of knowledge
in Business Intelligence (BI) that ranges from the business executive viewpoint
to insights promised by text mining. The expert authors know that BI is
about reducing the uncertainties of our business world. A timely and accurate
view into business conditions can minimize uncertainty.

The reduction of business and technical risk is the central theme of this
text. If data gives us the facts and information allows us to draw conclusions,
then intelligence provides the basis for making good business decisions. Information
technology can help you seize the information that is available.
Intelligence involves knowing information about your competitors, such
as their profitability and turnover rate. The most important thing to gain from
intelligence is knowledge of customers and potential customers. This knowledge
will help you to better serve customers and ensure that your service
offerings align with their needs. Performing an annual survey will not give you
this type of information. You need to know why people are or are not your
customers. If they are not your customers, whose are they? Have they heard
of your company? Are they familiar with your services or are they part of an
untapped market?

An IT organization is responsible for putting information in a place where
it can be mined by salespeople, product developers, and others within an
organization. One way to achieve this is through an information portal. An
information portal uses the same technology as Web search engines to find
and catalog information within your company giving access to everyone. IT
sets up pointers to the information, allowing people to turn it into intelligence.
Business decision makers need rapid access to information about their
customers, markets, investors, suppliers, governments, employees, and finances.
There are four critical success factors for strategically using and managing
IT. First, enterprises must be able to quantify the value of IT. They must
know how IT contributes to the creation of the value and wealth of their organization.
The second factor involves the ability to collect and organize intelligence,
both internally and externally. This intelligence includes information about
your market, your customers, and your potential customers. Third, enterprises
need to understand the wide spectrum of capability and productivity of IT
people within the same skill set. The final success factor is to invest in IT
people that can invent and create new tools or services. The internal and
external business information problem has existed for centuries — the best
hope for the future is the wise use of business intelligence tools.
Thomas L. Hill
Electronic Data Systems (EDS)
Fellow
Thomas Hill has the distinction of being an EDS Fellow, the highest level
of technical achievement in the corporation. He brings more than 30
years of extensive experience to EDS’ efforts for clients around the world.
EDS Fellows are visionary thinkers who represent the top echelon of EDS’
thought leadership capabilities. Fellows play a vital role in promoting
innovation at EDS and in extending EDS’ external reputation as a thought
leader and an innovative company through their work and engagements.

EDS, the leading global services company, provides strategy, implementation
and hosting for clients managing the business and technology complexities
of the digital economy. As the world’s largest outsourcing services
company, EDS, founded in 1962, is built on a heritage of delivery
excellence, industry knowledge, a world-class technical infrastructure
and the expertise of its people. EDS brings together the world’s best technologies
to address critical client business imperatives. It helps clients
eliminate boundaries, collaborate in new ways, establish their customers’
trust and continuously seek improvement. EDS, with its management-
consulting subsidiary, A.T. Kearney, serves more than 35,000 business
and government clients in 60 countries. EDS Fellows provide ongoing
support to a large number of EDS clients, including General Motors,
Sabre, Veterans Administration, Inland Revenue, British Petroleum, First
Health and Telecom New Zealand and are integrated into other clientfacing
engagements. This integration is critical to thoroughly diagnosing
their clients’ business challenges as well as developing innovative


Introduction
Focus and Content of this Book
Business Intelligence in the Digital Economy: Opportunities,
Limitations, and Risks
Wisdom grows in those who help others achieve greatness.
- Colle Davis
Who will build intelligence into your business processes? Organizations
that need to gain more efficiency and manage or reduce costs are looking to
Business Intelligence (BI) to address their requirements. This book can be
used as a tool to explore the vast parameters of the applications, problems,
and solutions related to BI. Contributing authors include management consultants,
researchers, and BI specialists from around the world. The book has an
extensive range of topics for practitioners and researchers who want to learn
about the state of the art and science in business intelligence and extend the
body of knowledge.

BI is important in helping companies stay ahead of the competition by
providing the means for quicker, more accurate and more informed decision
making. BI is a general term for applications, platforms, tools, and technologies
that support the process of exploring business data, data relationships,
and trends. BI applications provide companies with the means to gather and
analyze data that facilitates reporting, querying, and decision making. The most
agile BI products/services are not confined by industry classification and can
create an infinite number of possible applications for any business department
or a combination of departments.

Business Intelligence (BI) provides an executive with timely and accurate
information to better understand his or her business and to make more
informed, real-time business decisions. Full utilization of BI solutions can optimize
business processes and resources, improve proactive decision making,
and maximize profits/minimize costs. These solutions can create an infinite
number of possible applications for finance, competition monitoring, accounting,
marketing, product comparison, or a combination of a number of business
areas. The most agile BI solutions can be used in any industry and provide
an infinite number of value-increasing possibilities for any organization.
The purpose of this executive’s guide on Business Intelligence is to describe
what BI is; how it is being conducted and managed; and its major
opportunities, limitations, issues, and risks. It brings together some high-quality
expository discussions from experts in this field to identify, define, and
explore BI methodologies, systems, and approaches in order to understand
their opportunities, limitations and risks.

The audience of this book is MBA students, business executives, consultants,
seniors in an undergraduate business degree program, and students
in vocational/technical training institutes.
The scholarly value of this proposed book and its contribution will be to
the literature in information systems/e-business discipline. None of the current
books on the market address this topic from a holistic perspective. Some are
more geared toward knowledge management or artificial intelligence. Others
take a more computer science and engineering perspective or a statistical
analysis perspective.

CHAPTER OVERVIEW
Chapter I proposes that the initial perceptions of uncertainty and risk
relating to the decisions faced are unlikely to be modified, irrespective of the
quantity or quality of the information transmitted and processed by the decision
maker. Initial risk perceptions and decisions are fairly robust even when
confronted with contradictory information. Empirical evidence presented illustrates
that the decision maker may also construct his or her decision-making
behavior to constrain the opportunity for new information to alter the initial
perceptions and choices made. Chapter I thus explores the premise that increased
business intelligence reduces the risk inherent in decision making and
provides suggestions on the appropriate management of individuals involved
in information search activities.

Chapter II presents a high-level model for employing intelligent agents in
business management processes in order to gain competitive advantage by
timely, rapidly, and effectively using key, unfiltered, measurements to improve
cycle-time decision making. It conceptualizes the transition of intelligent agents
utilized in network performance management into the field of business and
management. The benefits of intelligent agents realized in telecommunications
networks, grid computing, and data visualization for exploratory analysis connected
to simulations should likewise be achievable in business management processes.

Chapter III describes the different flavors of data mining, including association
rules, classification and prediction, clustering and outlier analysis, customer
profiling, and how each of these can be used in practice to improve a
business’ understanding of its customers. The chapter concludes with a concise
technical overview of how each data-mining technology works. In addition,
a concise discussion of the knowledge-discovery process — from domain
analysis and data selection, to data preprocessing and transformation, to
the data mining itself, and finally the interpretation and evaluation of the results
as applied to the domain — is also provided along with the moral and legal
issues of knowledge discovery.

Chapter IV provides a German industry perspective with a good balance
of business and technology issues. Although system performance and
product efficiency are continuously increasing, the information and knowledge
capability of the enterprise often does not scale to the development of business
requirements. This often happens due to complex company structures,
fast growth or change of processes, and rising complexity of business information
needs on one hand and a slow and difficult IT-improvement process
on the other hand. The chapter illustrates which system architecture to use,
which logical application structure to develop, how to set up and integrate the
implementation project successfully, how to operate and improve these environments
continuously, and how to configure, improve, and maintain the reporting,
OLAP and HOLAP environments.

Chapter V presents an Intelligent Knowledge-Based Multi-Agent Architecture
for Collaboration (IKMAC) to enable such collaborations in B2B
e-Marketplaces. IKMAC is built upon existing bodies of knowledge in intelligent
agents, knowledge management, e-business, and XML and web services
standards. This chapter focuses on the translation of data, information,
and knowledge into XML documents by software agents, thereby creating
the foundation for knowledge representation and exchange by intelligent agents
that support collaborative work between business partners. Some illustrative
business examples of application in Collaborative Commerce, E-Supply Chains,
and electronic marketplaces and financial applications — credit analysis, bankruptcy
analysis — are also presented. IKMAC incorporates a consolidated
knowledge repository to store and retrieve knowledge, captured in XML
documents, to be used and shared by software agents within the multi-agent
architecture. The realization of the proposed architecture is explicated through
an infomediary-based e-Marketplace prototype in which agents facilitate collaboration
by exchanging their knowledge using XML and related sets of standards.

Chapter VI takes a closer look at text mining that is a collection of broad
techniques for analyzing text, extracting key components, and restructuring
them in manner suitable for analysis. As the demands for more effective Business
Intelligence (BI) techniques increases, BI practitioners find they must
expand the scope of their data to include unstructured text. To exploit those
information resources, techniques such as text mining are essential. This chapter
describes three fundamental techniques for text mining in business intelligence:
term extraction, information extraction, and link analysis; an outline of the
basic steps involved; characteristics of appropriate applications; and an overview
of its limitations. The limits and risks of all three techniques center around
the dependency on statistical techniques — the results of which vary by the
quality of available data, and linguistic analysis that is complex but cannot yet
analyze the full range of natural language encountered in business environments.

Chapter VII makes a step-by-step analysis of how one retail giant moved
quickly to solve a very real problem facing industry executives today, i.e.,
getting and manipulating necessary data from a large variety of diverse legacy
systems running on heterogeneous operating systems and platforms. The case
study shows how the organization evaluated available software packages
against internal development and nimbly adopted internal development to yield
an integrated system that gathers and manipulates data from diverse systems
using a common system architecture. The chapter also provides a valuable
insight into the area of reclamation of advertising revenue that is valued at 3%
of retail sales. The imperative this company faced was the loss of that revenue
due to the expiration of the claim period unless its proposed solution came
online as planned. The analysis shows, in detail, how a variety of systems’
data were linked in a highly unique but effective manner to create the system
that has value far greater than the sum of its parts.

Chapter VIII explores the opportunities to expand the forecasting and
business understanding capabilities of Business Intelligence (BI) tools by using
the system dynamics approach as a complement to simulate real-world
behavior. System dynamics take advantage of the information provided by BI
applications to model real-world under a “systems thinking” approach, improving
forecasts and contributing to a better understanding of the business
dynamics of any organization. It discusses how BI tools can support system
dynamics tools, supplying “analyzed and screened data” to models of realworld
situations that are illustrated by application examples such as Customer
Relationship Management (i.e., supporting the processes of acquiring, retaining,
and enhancing customers with a better understanding of their behavior),
Value-Based Management (i.e., understanding the dynamics of economic value
creation in an organization), and Balanced Scorecard (i.e., modeling a balanced
scorecard for a better insight of enterprise performance drivers).

Chapter IX explores data mining and its benefits and capabilities as a
key tool for obtaining vital business intelligence information. It includes an
overview of data mining, followed by its evolution, methods, technologies,
applications, and future. It discusses the technologies and techniques of data
mining, such as visual, spatial, human-centered, “vertical” (or application-specific),
constraint-based, and ubiquitous data mining (UDM) for mobile/distributed
environments. Examples of applications and practical uses of data
mining as it transitions from research prototypes to data-mining products, languages,
and standards are also presented in this chapter.

Chapter X focuses on the factors necessary for strategic BI success from
a managerial perspective. BI results from the various information and human
knowledge source systems, as well as the holistic view of the business processes
within an organization, with its goal being to maximize the resources,
and minimize the inefficiencies that are systematic within an organization. The
interrelated and non-sequential factors for BI success are discussed. The chapter
discusses the critical success factors that enable strategic BI success, i.e.,
business process of BI within an organization, managerial understanding of
data systems, accountability for BI, and execution on BI.

Chapter XI discusses the role of text mining (TM) in BI and clarifies the
interface between them. BI can benefit greatly from the bulk of knowledge
that stays hidden in the large amount of textual information existing in the organizational
environment. TM is a technology that provides the support to
extract patterns from texts. After interpreting these patterns, a business analyst
can reach useful insights to improve the organizational knowledge. Although
texts represent the largest part of the available information in a company,
just a small part of all Knowledge Discovery applications are in TM. By
means of a case study, this chapter shows an alternative of how TM can contribute
to BI. The case study presented, with the methodological approach
described and an adequate tool, can be used to guide an analyst in developing
similar applications. A discussion on future trends such as the approach that
uses concepts instead of words to represent documents supports the effectiveness
of TM as source of relevant knowledge.

Chapter XII is an explanatory study of a CRM application in a financial
services organization to understand decision-making in data warehousing and
related decision support systems (DSS), the authors find the DSS provided
by these systems limited and a difference in strategy selection between the
two groups of user, analysts and advisors, related to incentives. They recommend
an extended version of the DSS-decision performance model that includes
the individual characteristics of the user as a construct to better describe
the factors that influence individual decision-making performance and
includes metadata, explanations and qualitative data as explicit dimensions of
the DSS capability construct.

Chapter XIII is a two-part survey exploring the role of data integration
in E-CRM Analytics for both B2B and B2C firms. The first part of the survey
looks at the nature of the data integrated and the data architecture deployed
and the second part analyzes technology and organizational value added with
respect to the e-CRM initiative. Interestingly, (and as one’s intuition may lead
one to believe) they find that an organization that integrates data from multiple
customer touch points has significantly higher benefits, user satisfaction, and
return on its investment than organizations that do not do so. They propose an
e-CRM Value framework as a model for generating greater total benefits for
organizations engaging in e-CRM projects.
Mahesh Raisinghani, PhD, CEC
Editor
October 2003

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 Pages
 309 p
 File Type
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 ISBN
 1-59140-206-9 (hardcover)
 1-59140-280-8 (softcover)
 1-59140-207-7 (ebook)
 Copyright
 2004 by Idea Group Inc 


Table of Contents
Foreword ................. vii
Preface ...... x
Chapter I
Reducing Risk in Information Search Activities 1
Clare Brindley, Manchester Metropolitan University, UK
Bob Ritchie, Manchester Metropolitan University, UK
Chapter II
Intelligent Agents for Competitive Advantage: Requirements and Issues 25
Mahesh Raisinghani, University of Dallas, USA
John H. Nugent, University of Dallas, USA
Chapter III
Data Mining and Knowledge Discovery 35
Andi Baritchi, Corporate Data Systems, USA
Chapter IV
Enterprise Information Management  48
Ulfert Gartz, PA Consulting Group, Germany
Chapter V
An Intelligent Knowledge-Based Multi-Agent Architecture for
Collaboration (IKMAC) in B2B e-Marketplaces  76
Rahul Singh, University of North Carolina at Greensboro, USA
Lakshmi Iyer, University of North Carolina at Greensboro, USA
Al Salam, University of North Carolina at Greensboro, USA
Chapter VI
Text Mining in Business Intelligence  98
Dan Sullivan, The Ballston Group, USA
Chapter VII
Bypassing Legacy Systems Obstacles: How One Company Built
Its Intelligence to Identify and Collect Trade Allowances 111
James E. Skibo, University of Dallas, USA
Chapter VIII
Expanding Business Intelligence Power with System Dynamics  126
Edilberto Casado, Gerens Escuela de Gestión y Economía, Peru
Chapter IX
Data Mining and Business Intelligence: 
Tools, Technologies, and Applications  141
Jeffrey Hsu, Fairleigh Dickinson University, USA
Chapter X
Management Factors for Strategic BI Success  191
Somya Chaudhary, Bellsouth Telecommunications Inc., USA
Chapter XI
Transforming Textual Patterns into Knowledge  207
Hércules Antonio do Prado, Catholic University of Brasília,
Brazilian Enterprise for Agriculture Research, Brazil
José Palazzo Moreira de Oliveira, Federal University of
Rio Grande do Sul, Brazil
Edilson Ferneda, Catholic University of Brasília, Brazil
Leandro Krug Wives, Federal University of Rio Grande do Sul, Brazil
Edilberto Magalhães Silva, Brazilian Public News Agency, Brazil
Stanley Loh, Catholic University of Pelotas and Lutheran University
of Brazil, Brazil
Chapter XII
Understanding Decision-Making in Data Warehousing and Related
Decision Support Systems: An Explanatory Study of a Customer
Relationship Management Application  228
John D. Wells, Washington State University, USA
Traci J. Hess, Washington State University, USA
Chapter XIII
E-CRM Analytics: The Role of Data Integration  251
Hamid R. Nemati, University of North Carolina, USA
Christopher D. Barko, University of North Carolina, USA
Ashfaaq Moosa, University of North Carolina, USA
Glossary .............. 270
About the Authors ......... 277
Index ........... 285

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